We propose the first general PAC-Bayesian generalization bounds for adversarial robustness, that estimate, at test time, how much a model will be invariant to imperceptible perturbations in the input. Instead of deriving a worst-case analysis of the risk of a hypothesis over all the possible perturbations, we leverage the PAC-Bayesian framework to bound the averaged risk on the perturbations for majority votes (over the whole class of hypotheses). Our theoretically founded analysis has the advantage to provide general bounds (i) independent from the type of perturbations (i.e., the adversarial attacks), (ii) that are tight thanks to the PAC-Bayesian framework, (iii) that can be directly minimized during the learning phase to obtain a robust model on different attacks at test time.

Prediction tasks related to congestion are targeted at improving the level of service of the transportation network. With increasing access to larger datasets of higher resolution, the relevance of deep learning in such prediction tasks, is increasing. Several comprehensive survey papers in recent years have summarised the deep learning applications in the transportation domain. However, the system dynamics of the transportation network vary greatly between the non-congested state and the congested state -- thereby necessitating the need for a clear understanding of the challenges specific to congestion prediction. In this survey, we present the current state of deep learning applications in the tasks related to detection, prediction and propagation of congestion. Recurrent and non-recurrent congestion are discussed separately. Our survey leads us to uncover inherent challenges and gaps in the current state of research. Finally, we present some suggestions for future research directions as answers to the identified challenges.

Web-scale repositories of products, patents and scientific papers offer an opportunity for creating automated systems that scour millions of ideas and assist users in discovering inspirations and solutions. Yet the common representation of ideas is in the form of raw textual descriptions, lacking important structure that is required for supporting creative innovation. Prior work has pointed to the importance of functional structure -- capturing the mechanisms and purposes of inventions -- for allowing users to discover structural connections across ideas and creatively adapt existing technologies. However, the use of functional representations was either coarse and limited in expressivity, or dependent on curated knowledge bases with poor coverage and significant manual effort from users. To help bridge this gap and unlock the potential of large-scale idea mining, we propose a novel computational representation that automatically breaks up products into fine-grained functional facets. We train a model to extract these facets from a challenging real-world corpus of invention descriptions, and represent each product as a set of facet embeddings. We design similarity metrics that support granular matching between functional facets across ideas, and use them to build a novel functional search capability that enables expressive queries for mechanisms and purposes. We construct a graph capturing hierarchical relations between purposes and mechanisms across an entire corpus of products, and use the graph to help problem-solvers explore the design space around a focal problem and view related problem perspectives. In empirical user studies, our approach leads to a significant boost in search accuracy and in the quality of creative inspirations, outperforming strong baselines and state-of-art representations of product texts by 50-60%.

Now the search for life on Mars begins in earnest. After a seven-month, 292-million-mile journey, NASA's fastest and best-equipped rover ever--Perseverance--touched down safely Thursday on the red planet, NASA officials said. The $2.7 billion rover landed in an ancient lake bed called Jezero Crater at about 3:55 p.m. EST on Thursday, the jubilant officials said. The two-year Perseverance mission is the latest and most ambitious effort by NASA to find evidence of past life on Mars. The 1-ton, SUV-size rover will spend the next two years prospecting for evidence of ancient microbes.

After a thankfully uneventful seven-month journey, NASA's Mars 2020 mission is set to safely reach the Red Planet and insert itself into orbit on Thursday ahead of deploying the Perseverance rover and Ingenuity helicopter prototype that it's been toting down to the planet's surface in search for evidence of ancient microbial life. However, this expedition has been in the works for far longer than Perseverance has been travelling through interplanetary space. First announced in 2012, the mission marks the culmination of nearly a decade's work by hundreds of machinists, designers, rocket scientists and engineers at NASA's Jet Propulsion Lab. But not just anyone can get hired there, working for the world's premiere spacecraft production facility and building equipment that will grace the surfaces of neighboring planets. For Mohamed Abid, a Deputy Chief Mechanical Engineer on the Mars 2020 mission, the path to working at the JPL began in Tunisia, where he grew up.

NASA will today try and land its Perseverance rover on the surface of Mars in a crucial moment for the space agency's hopes of colonising the red planet. The descent of the $2.2billion car-sized spacecraft will be live streamed by NASA from 2:15 pm ET (7:15pm GMT) and will show Perseverance trying to endure the so-called'seven minutes of terror'. This refers to the tumultuous conditions which batter the craft as it enters the Martian atmosphere and approaches the surface. Temperatures are expected to exceed 2,000 F and a supersonic parachute will be deployed to try and slow the rover down from its entry speed of around 12,000mph -- quick enough to travel from London to New York in 15 minutes. Perseverance, if all goes to plan, will touch down at the base of an 820-foot-deep (250 meters) crater called Jezero, a former lake which was home to water 3.5 billion years ago. It will drill into Mars and collect geological specimens that will be cached across the planet and retrieved by a follow up mission around 2031.

Dating back to the Industrial Revolution, people have speculated that machines would render human ... [ ] work obsolete. Unlike in earlier eras, artificial intelligence will prove this prophecy true. "When looms weave by themselves, man's slavery will end." Stanford is hosting an event next month named "Intelligence Augmentation: AI Empowering People to Solve Global Challenges." This title is telling and typical.

There has been an increasing demand for demographic modelling and forecasting over the last few decades, driven by many developed countries are now suffering a rapid decline in mortality and fertility, leading to a significant increase in expenditures on health services for an ageing population and a shortage of future labour. A better understanding of the mortality and fertility patterns and trends is always of importance for all stakeholders in a society as the mortality forecasts, for example, play a vital role for the insurance and pensions industries in pricing their insurance products. The fertility predictions are also of great interest to the government and education sectors in planing children's welfare and educational services. Unlike the biological and the medical methods, statisticians have developed very different and purely mathematical methods to model the demographic patterns and trends which are well-documented by Preston et al. (2000). The history of demographic modelling with the mathematical approaches can be traced back to some deterministic models proposed in the midnineteenth century, see, for example, Gompertz (1825) and Makeham (1860). The deterministic models are, however, restricted with few fixed factors and have no stochastic process considered owing to the lack of computing capability in that early period.

High dimensional data can contain multiple scales of variance. Analysis tools that preferentially operate at one scale can be ineffective at capturing all the information present in this cross-scale complexity. We propose a multiscale joint characterization approach designed to exploit synergies between global and local approaches to dimensionality reduction. We illustrate this approach using Principal Components Analysis (PCA) to characterize global variance structure and t-stochastic neighbor embedding (t-sne) to characterize local variance structure. Using both synthetic images and real-world imaging spectroscopy data, we show that joint characterization is capable of detecting and isolating signals which are not evident from either PCA or t-sne alone. Broadly, t-sne is effective at rendering a randomly oriented low-dimensional map of local clusters, and PCA renders this map interpretable by providing global, physically meaningful structure. This approach is illustrated using imaging spectroscopy data, and may prove particularly useful for other geospatial data given robust local variance structure due to spatial autocorrelation and physical interpretability of global variance structure due to spectral properties of Earth surface materials. However, the fundamental premise could easily be extended to other high dimensional datasets, including image time series and non-image data.

Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users. This results in recommendations that are highly similar to the ones users are already exposed to, resulting in their isolation inside familiar but insulated information silos. In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. We focus on the problem of political content recommendation, while addressing a general problem applicable to personalization tasks in other social and information networks. For recommending political content on social networks, we first propose a new model to estimate the ideological positions for both users and the content they share, which is able to recover ideological positions with high accuracy. Based on these estimated positions, we generate diversified personalized recommendations using our new random-walk based recommendation algorithm. With experimental evaluations on large datasets of Twitter discussions, we show that our method based on \emph{random walks with erasure} is able to generate more ideologically diverse recommendations. Our approach does not depend on the availability of labels regarding the bias of users or content producers. With experiments on open benchmark datasets from other social and information networks, we also demonstrate the effectiveness of our method in recommending diverse long-tail items.